Advancing personalized education with large language models

The rapid advancement of artificial intelligence (AI), and particularly of Large Language Models (LLMs) such as the GPT family, has introduced new paradigms in data extraction and analysis, leading to the development of innovative applications.In the field of education specifically, AI is emerging as a fast-growing scientific domain that integrates machine learning, data analysis, and pedagogical practices with the goal of enhancing the learning process.However, the speed of this evolution and the broad range of its applications often act as barriers to its integration in education, especially by educators who are not familiar with its practical use or how to embed it effectively in their teaching practices.In this context, LLMs and adaptive learning techniques can serve as intermediaries between teachers, students, and AI systems.This approach enables personalized instruction, dynamic adaptation of learning paths, and meaningful human-machine collaboration in ways that were not previously possible.Nevertheless, despite the promising capabilities of this technology, several challenges remain.These include the design of user-friendly and functional interfaces, the improvement of algorithmic efficiency, and the assurance of ethical use of AI in education.i